Overview

Dataset statistics

Number of variables14
Number of observations400
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.9 KiB
Average record size in memory112.3 B

Variable types

Numeric10
Categorical4

Alerts

Al is highly overall correlated with Class and 2 other fieldsHigh correlation
Bu is highly overall correlated with Hemo and 1 other fieldsHigh correlation
Class is highly overall correlated with Al and 5 other fieldsHigh correlation
Hemo is highly overall correlated with Al and 4 other fieldsHigh correlation
Htn is highly overall correlated with Class and 2 other fieldsHigh correlation
Rbcc is highly overall correlated with Class and 3 other fieldsHigh correlation
Sc is highly overall correlated with Al and 5 other fieldsHigh correlation
Sg is highly overall correlated with ClassHigh correlation
Al has 199 (49.8%) zerosZeros
Su has 339 (84.8%) zerosZeros

Reproduction

Analysis started2024-07-26 12:25:33.437043
Analysis finished2024-07-26 12:25:49.716221
Duration16.28 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Bp
Real number (ℝ)

Distinct11
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.455
Minimum50
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-07-26T17:55:49.874797image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q170
median78
Q380
95-th percentile100
Maximum180
Range130
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.476536
Coefficient of variation (CV)0.17626755
Kurtosis9.0090714
Mean76.455
Median Absolute Deviation (MAD)8
Skewness1.6329366
Sum30582
Variance181.61702
MonotonicityNot monotonic
2024-07-26T17:55:50.039383image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
80 116
29.0%
70 112
28.0%
60 71
17.8%
90 53
13.2%
100 25
 
6.2%
76 12
 
3.0%
50 5
 
1.2%
110 3
 
0.8%
140 1
 
0.2%
180 1
 
0.2%
ValueCountFrequency (%)
50 5
 
1.2%
60 71
17.8%
70 112
28.0%
76 12
 
3.0%
80 116
29.0%
90 53
13.2%
100 25
 
6.2%
110 3
 
0.8%
120 1
 
0.2%
140 1
 
0.2%
ValueCountFrequency (%)
180 1
 
0.2%
140 1
 
0.2%
120 1
 
0.2%
110 3
 
0.8%
100 25
 
6.2%
90 53
13.2%
80 116
29.0%
76 12
 
3.0%
70 112
28.0%
60 71
17.8%

Sg
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
1.02
153 
1.01
84 
1.025
81 
1.015
75 
1.005
 
7

Length

Max length5
Median length4
Mean length4.4075
Min length4

Characters and Unicode

Total characters1763
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.02
2nd row1.02
3rd row1.01
4th row1.005
5th row1.01

Common Values

ValueCountFrequency (%)
1.02 153
38.2%
1.01 84
21.0%
1.025 81
20.2%
1.015 75
18.8%
1.005 7
 
1.8%

Length

2024-07-26T17:55:50.225885image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-26T17:55:50.409368image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1.02 153
38.2%
1.01 84
21.0%
1.025 81
20.2%
1.015 75
18.8%
1.005 7
 
1.8%

Most occurring characters

ValueCountFrequency (%)
1 559
31.7%
0 407
23.1%
. 400
22.7%
2 234
13.3%
5 163
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1363
77.3%
Other Punctuation 400
 
22.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 559
41.0%
0 407
29.9%
2 234
17.2%
5 163
 
12.0%
Other Punctuation
ValueCountFrequency (%)
. 400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1763
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 559
31.7%
0 407
23.1%
. 400
22.7%
2 234
13.3%
5 163
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1763
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 559
31.7%
0 407
23.1%
. 400
22.7%
2 234
13.3%
5 163
 
9.2%

Al
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.015
Minimum0
Maximum5
Zeros199
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-07-26T17:55:50.582904image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2723294
Coefficient of variation (CV)1.2535265
Kurtosis-0.035456005
Mean1.015
Median Absolute Deviation (MAD)1
Skewness1.0650989
Sum406
Variance1.6188221
MonotonicityNot monotonic
2024-07-26T17:55:50.743504image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 199
49.8%
1 90
22.5%
2 43
 
10.8%
3 43
 
10.8%
4 24
 
6.0%
5 1
 
0.2%
ValueCountFrequency (%)
0 199
49.8%
1 90
22.5%
2 43
 
10.8%
3 43
 
10.8%
4 24
 
6.0%
5 1
 
0.2%
ValueCountFrequency (%)
5 1
 
0.2%
4 24
 
6.0%
3 43
 
10.8%
2 43
 
10.8%
1 90
22.5%
0 199
49.8%

Su
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.395
Minimum0
Maximum5
Zeros339
Zeros (%)84.8%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-07-26T17:55:50.885121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0400381
Coefficient of variation (CV)2.6330078
Kurtosis6.3687605
Mean0.395
Median Absolute Deviation (MAD)0
Skewness2.700055
Sum158
Variance1.0816792
MonotonicityNot monotonic
2024-07-26T17:55:51.039708image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 339
84.8%
2 18
 
4.5%
3 14
 
3.5%
4 13
 
3.2%
1 13
 
3.2%
5 3
 
0.8%
ValueCountFrequency (%)
0 339
84.8%
1 13
 
3.2%
2 18
 
4.5%
3 14
 
3.5%
4 13
 
3.2%
5 3
 
0.8%
ValueCountFrequency (%)
5 3
 
0.8%
4 13
 
3.2%
3 14
 
3.5%
2 18
 
4.5%
1 13
 
3.2%
0 339
84.8%

Rbc
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
1.0
353 
0.0
47 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1200
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 353
88.2%
0.0 47
 
11.8%

Length

2024-07-26T17:55:51.217233image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-26T17:55:51.358854image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 353
88.2%
0.0 47
 
11.8%

Most occurring characters

ValueCountFrequency (%)
0 447
37.2%
. 400
33.3%
1 353
29.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 800
66.7%
Other Punctuation 400
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 447
55.9%
1 353
44.1%
Other Punctuation
ValueCountFrequency (%)
. 400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 447
37.2%
. 400
33.3%
1 353
29.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 447
37.2%
. 400
33.3%
1 353
29.4%

Bu
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.4055
Minimum1.5
Maximum391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-07-26T17:55:51.530369image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile17
Q127
median44
Q361.75
95-th percentile158.2
Maximum391
Range389.5
Interquartile range (IQR)34.75

Descriptive statistics

Standard deviation49.28597
Coefficient of variation (CV)0.85855833
Kurtosis9.9595113
Mean57.4055
Median Absolute Deviation (MAD)17
Skewness2.6999774
Sum22962.2
Variance2429.1069
MonotonicityNot monotonic
2024-07-26T17:55:51.754767image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 20
 
5.0%
46 15
 
3.8%
25 13
 
3.2%
19 11
 
2.8%
40 10
 
2.5%
15 9
 
2.2%
48 9
 
2.2%
50 9
 
2.2%
18 9
 
2.2%
32 8
 
2.0%
Other values (108) 287
71.8%
ValueCountFrequency (%)
1.5 1
 
0.2%
10 2
 
0.5%
15 9
2.2%
16 7
1.8%
17 7
1.8%
18 9
2.2%
19 11
2.8%
20 7
1.8%
21 1
 
0.2%
22 6
1.5%
ValueCountFrequency (%)
391 1
0.2%
322 1
0.2%
309 1
0.2%
241 1
0.2%
235 1
0.2%
223 1
0.2%
219 1
0.2%
217 1
0.2%
215 1
0.2%
208 1
0.2%

Sc
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.07235
Minimum0.4
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-07-26T17:55:51.960218image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.5
Q10.9
median1.4
Q33.07
95-th percentile11.805
Maximum76
Range75.6
Interquartile range (IQR)2.17

Descriptive statistics

Standard deviation5.6174901
Coefficient of variation (CV)1.8284018
Kurtosis82.912221
Mean3.07235
Median Absolute Deviation (MAD)0.7
Skewness7.6731605
Sum1228.94
Variance31.556195
MonotonicityNot monotonic
2024-07-26T17:55:52.172651image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 40
 
10.0%
1.1 24
 
6.0%
0.5 23
 
5.8%
1 23
 
5.8%
0.9 22
 
5.5%
0.7 22
 
5.5%
0.6 18
 
4.5%
3.07 17
 
4.2%
0.8 17
 
4.2%
2.2 10
 
2.5%
Other values (75) 184
46.0%
ValueCountFrequency (%)
0.4 1
 
0.2%
0.5 23
5.8%
0.6 18
4.5%
0.7 22
5.5%
0.8 17
4.2%
0.9 22
5.5%
1 23
5.8%
1.1 24
6.0%
1.2 40
10.0%
1.3 8
 
2.0%
ValueCountFrequency (%)
76 1
0.2%
48.1 1
0.2%
32 1
0.2%
24 1
0.2%
18.1 1
0.2%
18 1
0.2%
16.9 1
0.2%
16.4 1
0.2%
15.2 1
0.2%
15 1
0.2%

Sod
Real number (ℝ)

Distinct35
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.52902
Minimum4.5
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-07-26T17:55:52.526730image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile127
Q1135
median137.53
Q3141
95-th percentile147
Maximum163
Range158.5
Interquartile range (IQR)6

Descriptive statistics

Standard deviation9.2042735
Coefficient of variation (CV)0.066926043
Kurtosis109.76332
Mean137.52902
Median Absolute Deviation (MAD)2.53
Skewness-7.9011825
Sum55011.61
Variance84.71865
MonotonicityNot monotonic
2024-07-26T17:55:52.721182image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
137.53 87
21.8%
135 40
 
10.0%
140 25
 
6.2%
141 22
 
5.5%
139 21
 
5.2%
142 20
 
5.0%
138 20
 
5.0%
137 19
 
4.8%
150 17
 
4.2%
136 17
 
4.2%
Other values (25) 112
28.0%
ValueCountFrequency (%)
4.5 1
 
0.2%
104 1
 
0.2%
111 1
 
0.2%
113 2
0.5%
114 2
0.5%
115 1
 
0.2%
120 2
0.5%
122 2
0.5%
124 3
0.8%
125 2
0.5%
ValueCountFrequency (%)
163 1
 
0.2%
150 17
4.2%
147 13
3.2%
146 10
 
2.5%
145 11
2.8%
144 9
 
2.2%
143 4
 
1.0%
142 20
5.0%
141 22
5.5%
140 25
6.2%

Pot
Real number (ℝ)

Distinct41
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.62785
Minimum2.5
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-07-26T17:55:52.917684image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.495
Q14
median4.63
Q34.8
95-th percentile5.505
Maximum47
Range44.5
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation2.8197828
Coefficient of variation (CV)0.60930731
Kurtosis182.88847
Mean4.62785
Median Absolute Deviation (MAD)0.37
Skewness13.100506
Sum1851.14
Variance7.9511753
MonotonicityNot monotonic
2024-07-26T17:55:53.094233image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
4.63 88
22.0%
3.5 30
 
7.5%
5 30
 
7.5%
4.9 27
 
6.8%
4.7 17
 
4.2%
4.8 16
 
4.0%
4 14
 
3.5%
4.1 14
 
3.5%
4.4 14
 
3.5%
3.9 14
 
3.5%
Other values (31) 136
34.0%
ValueCountFrequency (%)
2.5 2
 
0.5%
2.7 1
 
0.2%
2.8 1
 
0.2%
2.9 3
 
0.8%
3 2
 
0.5%
3.2 3
 
0.8%
3.3 3
 
0.8%
3.4 5
 
1.2%
3.5 30
7.5%
3.6 8
 
2.0%
ValueCountFrequency (%)
47 1
 
0.2%
39 1
 
0.2%
7.6 1
 
0.2%
6.6 1
 
0.2%
6.5 2
0.5%
6.4 1
 
0.2%
6.3 3
0.8%
5.9 2
0.5%
5.8 2
0.5%
5.7 4
1.0%

Hemo
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5269
Minimum3.1
Maximum17.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-07-26T17:55:53.277716image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile7.9
Q110.875
median12.53
Q314.625
95-th percentile16.8
Maximum17.8
Range14.7
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation2.7161715
Coefficient of variation (CV)0.2168271
Kurtosis-0.091501924
Mean12.5269
Median Absolute Deviation (MAD)1.87
Skewness-0.35957077
Sum5010.76
Variance7.3775874
MonotonicityNot monotonic
2024-07-26T17:55:53.482195image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.53 52
 
13.0%
15 16
 
4.0%
10.9 8
 
2.0%
13.6 7
 
1.8%
13 7
 
1.8%
9.8 7
 
1.8%
11.1 7
 
1.8%
10.3 6
 
1.5%
11.3 6
 
1.5%
13.9 6
 
1.5%
Other values (106) 278
69.5%
ValueCountFrequency (%)
3.1 1
0.2%
4.8 1
0.2%
5.5 1
0.2%
5.6 1
0.2%
5.8 1
0.2%
6 2
0.5%
6.1 1
0.2%
6.2 1
0.2%
6.3 1
0.2%
6.6 1
0.2%
ValueCountFrequency (%)
17.8 3
0.8%
17.7 1
 
0.2%
17.6 1
 
0.2%
17.5 1
 
0.2%
17.4 2
0.5%
17.3 1
 
0.2%
17.2 2
0.5%
17.1 2
0.5%
17 4
1.0%
16.9 2
0.5%

Wbcc
Real number (ℝ)

Distinct90
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8406.09
Minimum2200
Maximum26400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-07-26T17:55:53.697619image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum2200
5-th percentile4700
Q16975
median8406
Q39400
95-th percentile12400
Maximum26400
Range24200
Interquartile range (IQR)2425

Descriptive statistics

Standard deviation2523.22
Coefficient of variation (CV)0.30016571
Kurtosis9.4128464
Mean8406.09
Median Absolute Deviation (MAD)1194
Skewness1.8889257
Sum3362436
Variance6366639
MonotonicityNot monotonic
2024-07-26T17:55:53.908029image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8406 106
26.5%
9800 11
 
2.8%
6700 10
 
2.5%
9600 9
 
2.2%
7200 9
 
2.2%
9200 9
 
2.2%
6900 8
 
2.0%
5800 8
 
2.0%
11000 8
 
2.0%
7800 7
 
1.8%
Other values (80) 215
53.8%
ValueCountFrequency (%)
2200 1
 
0.2%
2600 1
 
0.2%
3800 2
 
0.5%
4100 1
 
0.2%
4200 3
0.8%
4300 6
1.5%
4500 3
0.8%
4700 4
1.0%
4900 1
 
0.2%
5000 5
1.2%
ValueCountFrequency (%)
26400 1
0.2%
21600 1
0.2%
19100 1
0.2%
18900 1
0.2%
16700 1
0.2%
16300 1
0.2%
15700 1
0.2%
15200 2
0.5%
14900 1
0.2%
14600 2
0.5%

Rbcc
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.708275
Minimum2.1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-07-26T17:55:54.100542image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum2.1
5-th percentile3.2
Q14.5
median4.71
Q35.1
95-th percentile6.105
Maximum8
Range5.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.84031518
Coefficient of variation (CV)0.17847623
Kurtosis1.0628788
Mean4.708275
Median Absolute Deviation (MAD)0.29
Skewness-0.2261583
Sum1883.31
Variance0.7061296
MonotonicityNot monotonic
2024-07-26T17:55:54.303007image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
4.71 131
32.8%
5.2 18
 
4.5%
4.5 16
 
4.0%
4.9 14
 
3.5%
4.7 11
 
2.8%
3.9 10
 
2.5%
5 10
 
2.5%
4.8 10
 
2.5%
4.6 9
 
2.2%
3.4 9
 
2.2%
Other values (36) 162
40.5%
ValueCountFrequency (%)
2.1 2
0.5%
2.3 1
 
0.2%
2.4 1
 
0.2%
2.5 2
0.5%
2.6 2
0.5%
2.7 2
0.5%
2.8 2
0.5%
2.9 2
0.5%
3 3
0.8%
3.1 2
0.5%
ValueCountFrequency (%)
8 1
 
0.2%
6.5 5
1.2%
6.4 5
1.2%
6.3 4
1.0%
6.2 5
1.2%
6.1 8
2.0%
6 4
1.0%
5.9 8
2.0%
5.8 7
1.8%
5.7 5
1.2%

Htn
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
0.0
251 
1.0
147 
0.37
 
2

Length

Max length4
Median length3
Mean length3.005
Min length3

Characters and Unicode

Total characters1202
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 251
62.7%
1.0 147
36.8%
0.37 2
 
0.5%

Length

2024-07-26T17:55:54.492501image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-26T17:55:54.647113image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 251
62.7%
1.0 147
36.8%
0.37 2
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 651
54.2%
. 400
33.3%
1 147
 
12.2%
3 2
 
0.2%
7 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 802
66.7%
Other Punctuation 400
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 651
81.2%
1 147
 
18.3%
3 2
 
0.2%
7 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1202
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 651
54.2%
. 400
33.3%
1 147
 
12.2%
3 2
 
0.2%
7 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 651
54.2%
. 400
33.3%
1 147
 
12.2%
3 2
 
0.2%
7 2
 
0.2%

Class
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
1
250 
0
150 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters400
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 250
62.5%
0 150
37.5%

Length

2024-07-26T17:55:54.802697image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-26T17:55:54.944319image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1 250
62.5%
0 150
37.5%

Most occurring characters

ValueCountFrequency (%)
1 250
62.5%
0 150
37.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 250
62.5%
0 150
37.5%

Most occurring scripts

ValueCountFrequency (%)
Common 400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 250
62.5%
0 150
37.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 250
62.5%
0 150
37.5%

Interactions

2024-07-26T17:55:47.695549image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:34.380970image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:35.800368image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:37.193642image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:38.721554image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:40.453921image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:41.895066image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:43.388072image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:44.707541image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:46.300282image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:47.835176image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:34.525779image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:35.936004image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:37.348230image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:38.875142image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:40.596540image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:42.040675image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:43.520719image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:44.849163image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:46.440905image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:48.015693image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:34.660449image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:36.065686image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:37.480875image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:39.014772image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:40.734169image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:42.192269image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:43.648376image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:44.983804image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:46.569561image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:48.186238image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:34.794060image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:36.200300image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:37.613517image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:39.159382image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:40.874797image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:42.334892image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:43.771048image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:45.124429image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:46.699214image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:48.341822image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:34.948646image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:36.348902image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:37.763118image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:39.377798image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:41.030377image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:42.493465image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:43.914662image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:45.280012image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:46.849814image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:48.490428image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:35.101240image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:36.494512image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:37.904737image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:39.626136image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:41.181974image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:42.651071image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:44.048305image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:45.426620image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:47.012401image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:48.640023image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:35.251835image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:36.646106image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:38.189976image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:39.796679image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:41.337558image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:42.807625image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:44.191921image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:45.746763image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:47.159982image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:48.764692image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:35.380492image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:36.782742image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:38.311652image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:39.924339image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:41.470204image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:42.937277image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:44.308611image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:45.871428image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:47.284651image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:48.908306image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:35.526103image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:36.927354image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:38.453271image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:40.079922image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:41.619804image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:43.094858image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:44.442251image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:46.019035image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:47.431258image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:49.041949image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:35.657749image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:37.055011image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:38.585921image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:40.288364image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:41.754443image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:43.237475image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:44.568915image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:46.157665image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-26T17:55:47.559914image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-07-26T17:55:55.069956image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
AlBpBuClassHemoHtnPotRbcRbccScSgSodSuWbcc
Al1.0000.1860.4850.720-0.6440.3830.0670.379-0.4990.5920.285-0.4460.3220.172
Bp0.1861.0000.1800.440-0.2530.2390.0650.189-0.2250.2860.133-0.1030.1880.035
Bu0.4850.1801.0000.373-0.5450.3070.1780.256-0.4390.7190.145-0.3730.1630.090
Class0.7200.4400.3731.000-0.7830.5900.0330.270-0.6310.6850.695-0.4800.3270.208
Hemo-0.644-0.253-0.545-0.7831.0000.401-0.0620.2790.692-0.6720.2790.456-0.213-0.160
Htn0.3830.2390.3070.5900.4011.0000.0750.123-0.5340.5680.243-0.4030.2900.111
Pot0.0670.0650.1780.033-0.0620.0751.0000.000-0.0480.1140.0000.0030.023-0.064
Rbc0.3790.1890.2560.2700.2790.1230.0001.0000.186-0.2580.2800.217-0.121-0.030
Rbcc-0.499-0.225-0.439-0.6310.692-0.534-0.0480.1861.000-0.5420.2510.352-0.227-0.160
Sc0.5920.2860.7190.685-0.6720.5680.114-0.258-0.5421.0000.102-0.4580.2610.125
Sg0.2850.1330.1450.6950.2790.2430.0000.2800.2510.1021.0000.351-0.334-0.220
Sod-0.446-0.103-0.373-0.4800.456-0.4030.0030.2170.352-0.4580.3511.000-0.1700.007
Su0.3220.1880.1630.327-0.2130.2900.023-0.121-0.2270.261-0.334-0.1701.0000.211
Wbcc0.1720.0350.0900.208-0.1600.111-0.064-0.030-0.1600.125-0.2200.0070.2111.000

Missing values

2024-07-26T17:55:49.260364image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-26T17:55:49.566571image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

BpSgAlSuRbcBuScSodPotHemoWbccRbccHtnClass
080.01.0201.00.01.036.01.2137.534.6315.47800.05.201.01
150.01.0204.00.01.018.00.8137.534.6311.36000.04.710.01
280.01.0102.03.01.053.01.8137.534.639.67500.04.710.01
370.01.0054.00.01.056.03.8111.002.5011.26700.03.901.01
480.01.0102.00.01.026.01.4137.534.6311.67300.04.600.01
590.01.0153.00.01.025.01.1142.003.2012.27800.04.401.01
670.01.0100.00.01.054.024.0104.004.0012.48406.04.710.01
776.01.0152.04.01.031.01.1137.534.6312.46900.05.000.01
8100.01.0153.00.01.060.01.9137.534.6310.89600.04.001.01
990.01.0202.00.00.0107.07.2114.003.709.512100.03.701.01
BpSgAlSuRbcBuScSodPotHemoWbccRbccHtnClass
39080.01.0250.00.01.025.00.8135.03.715.06300.05.30.00
39180.01.0250.00.01.016.01.1142.04.115.65800.06.30.00
39280.01.0200.00.01.048.01.2147.04.314.86600.05.50.00
39360.01.0250.00.01.045.00.7141.04.413.07400.05.40.00
39480.01.0200.00.01.046.00.8139.05.014.19500.04.60.00
39580.01.0200.00.01.049.00.5150.04.915.76700.04.90.00
39670.01.0250.00.01.031.01.2141.03.516.57800.06.20.00
39780.01.0200.00.01.026.00.6137.04.415.86600.05.40.00
39860.01.0250.00.01.050.01.0135.04.914.27200.05.90.00
39980.01.0250.00.01.018.01.1141.03.515.86800.06.10.00